Synthesis of a Feedback Controller by the Network Operator Method for a Mobile Robot Rosbot in Gazebo Environment

Abstract

The article presents an approach based on machine learning with symbolic regression for the synthesis of a stabilization system for a mobile robot. This approach is universal and allows you to numerically solve the synthesis problem in a general setting without the need to form a training sample, instead relying only on the value of the functional. The synthesis is implemented to stabilize the mobile robot Rosbot in the Gazebo simulation environment. The feedback stabilization system is received by the network operator method. The advantage of the method is that it can be applied to a control object of any complexity and linearity.

Keywords

• control synthesis
• stabilization
• machine learning
• symbolic regression
• Gazebo mobile robot